scholarly journals How Secure Are Good Loans: Validating Loan-Granting Decisions And Predicting Default Rates On Consumer Loans

2002 ◽  
Vol 6 (3) ◽  
pp. 65-84 ◽  
Author(s):  
Jozef Zurada ◽  
Martin Zurada
Keyword(s):  
Data Mining ◽  
Credit Risk ◽  
Banking Industry ◽  
Financial Institution ◽  
Credit Scoring ◽  
Risk Groups ◽  
Consumer Loans ◽  
Consumer Lending ◽  
Default Rates ◽  
Scoring Accuracy

The failure or success of the banking industry depends largely on the industrys ability to properly evaluate credit risk. In the consumer-lending context, the banks goal is to maximize income by issuing as many good loans to consumers as possible while avoiding losses associated with bad loans. Mistakes could severely affect profits because the losses associated with one bad loan may undermine the income earned on many good loans. Therefore banks carefully evaluate the financial status of each customer as well as their credit worthiness and weigh them against the banks internal loan-granting policies. Recognizing that even a small improvement in credit scoring accuracy translates into significant future savings, the banking industry and the scientific community have been employing various machine learning and traditional statistical techniques to improve credit risk prediction accuracy.This paper examines historical data from consumer loans issued by a financial institution to individuals that the financial institution deemed to be qualified customers. The data consists of the financial attributes of each customer and includes a mixture of loans that the customers paid off and defaulted upon. The paper uses three different data mining techniques (decision trees, neural networks, logit regression) and the ensemble model, which combines the three techniques, to predict whether a particular customer defaulted or paid off his/her loan. The paper then compares the effectiveness of each technique and analyzes the risk of default inherent in each loan and group of loans. The data mining classification techniques and analysis can enable banks to more precisely classify consumers into various credit risk groups. Knowing what risk group a consumer falls into would allow a bank to fine tune its lending policies by recognizing high risk groups of consumers to whom loans should not be issued, and identifying safer loans that should be issued, on terms commensurate with the risk of default.

Beyond Classification

2008 ◽  
pp. 1855-1876
Author(s):  
Anna Olecka
Keyword(s):  
Data Mining ◽  
Credit Risk ◽  
Credit Card ◽  
Classification Scheme ◽  
Credit Scoring ◽  
Complex Nature ◽  
Acquisition Process ◽  
Business Objective ◽  
Simplifying Assumptions ◽  
History Of

This chapter will focus on challenges in modeling credit risk for new accounts acquisition process in the credit card industry. First section provides an overview and a brief history of credit scoring. The second section looks at some of the challenges specific to the credit industry. In many of these applications business objective is tied only indirectly to the classification scheme. Opposing objectives, such as response, profit and risk, often play a tug of war with each other. Solving a business problem of such complex nature often requires a multiple of models working jointly. Challenges to data mining lie in exploring solutions that go beyond traditional, well-documented methodology and need for simplifying assumptions; often necessitated by the reality of dataset sizes and/or implementation issues. Examples of such challenges form an illustrative example of a compromise between data mining theory and applications.

Beyond Classification

2011 ◽  
pp. 139-161
Author(s):  
Anna Olecka
Keyword(s):  
Data Mining ◽  
Credit Risk ◽  
Credit Card ◽  
Classification Scheme ◽  
Credit Scoring ◽  
Complex Nature ◽  
Acquisition Process ◽  
Business Objective ◽  
Simplifying Assumptions ◽  
History Of

This chapter will focus on challenges in modeling credit risk for new accounts acquisition process in the credit card industry. First section provides an overview and a brief history of credit scoring. The second section looks at some of the challenges specific to the credit industry. In many of these applications business objective is tied only indirectly to the classification scheme. Opposing objectives, such as response, profit and risk, often play a tug of war with each other. Solving a business problem of such complex nature often requires a multiple of models working jointly. Challenges to data mining lie in exploring solutions that go beyond traditional, well-documented methodology and need for simplifying assumptions; often necessitated by the reality of dataset sizes and/or implementation issues. Examples of such challenges form an illustrative example of a compromise between data mining theory and applications.

scholarly journals Geographically Weighted Logistic Regression Applied to Credit Scoring Models

2017 ◽  
Vol 28 (73) ◽  
pp. 93-112 ◽  
Author(s):  
Pedro Henrique Melo Albuquerque ◽  
◽  
Fabio Augusto Scalet Medina ◽  
Alan Ricardo da Silva ◽  
Keyword(s):  
Logistic Regression ◽  
Credit Risk ◽  
Predictive Power ◽  
Financial Institution ◽  
Credit Scoring ◽  
Real Data ◽  
Monetary Value ◽  
Weighted Logistic Regression ◽  
Geographically Weighted Logistic Regression ◽  
Financial Losses

Abstract This study used real data from a Brazilian financial institution on transactions involving Consumer Direct Credit (CDC), granted to clients residing in the Distrito Federal (DF), to construct credit scoring models via Logistic Regression and Geographically Weighted Logistic Regression (GWLR) techniques. The aims were: to verify whether the factors that influence credit risk differ according to the borrower’s geographic location; to compare the set of models estimated via GWLR with the global model estimated via Logistic Regression, in terms of predictive power and financial losses for the institution; and to verify the viability of using the GWLR technique to develop credit scoring models. The metrics used to compare the models developed via the two techniques were the AICc informational criterion, the accuracy of the models, the percentage of false positives, the sum of the value of false positive debt, and the expected monetary value of portfolio default compared with the monetary value of defaults observed. The models estimated for each region in the DF were distinct in their variables and coefficients (parameters), with it being concluded that credit risk was influenced differently in each region in the study. The Logistic Regression and GWLR methodologies presented very close results, in terms of predictive power and financial losses for the institution, and the study demonstrated viability in using the GWLR technique to develop credit scoring models for the target population in the study.

scholarly journals Can credit scoring models prevent default payments in the banking industry in the period of financial crisis?

2013 ◽  
Vol 2 (1) ◽  
pp. 32-38
Author(s):  
Violeta Madzova ◽  
Nehat Ramadini
Keyword(s):  
Credit Risk ◽  
Financial Crises ◽  
Scientific Method ◽  
World Economy ◽  
Banking Industry ◽  
Credit Scoring ◽  
Management Tool ◽  
Credit Default ◽  
Internal Information ◽  
Loan Officers

Credit scoring is a scientific method of assessing the credit risk associated with new credit applications as well as for monitoring of the credit risk in the process of the loan payment. Therefore credit scoring models developed in the banks based on their internal information system, as well as the credit info system in the country, can help the banks to ensure more consistent underwriting and can provide management with a more insightful measure of credit risk. While credit scoring could be a valuable risk management tool in virtually any bank setting, it is probably not wide accepted in the banks with more fundamental underwriting problems such as inexperienced loan officers, inadequate credit procedures, underdeveloped internal and external credit info – system , persistent arrears problems, etc. However credit scoring is only useful , and can make reliable predictions , if the credit scoring models are properly made, data base updated and credit scoring limitations are properly understood, which is very difficult in the turbulent economic environment as it is the world economy in the recent years. Therefore, the paper objective is to analyze if the banking industry can prevent credit default payments through the implementation of the credit scoring models and what are the parameters and the weights that need to be incorporated, so that the models to be more effective in the period of financial crises.

Collision of NPLs on the Financial Performance of Commercial Banks: A Case Study of Ethiopia

2019 ◽  
Vol 3 (1) ◽  
pp. 1-11
Author(s):  
Hailu Megersa Tola ◽  
D. Guna Sankar
Keyword(s):  
Credit Risk ◽  
Financial Performance ◽  
Commercial Banks ◽  
Banking Industry ◽  
Financial Institution ◽  
The Will ◽  
Survival And Growth ◽  
Potential Losses ◽  
Present Case Study

Credit risk in banking relates to the possibility that loans will not be paid or that investments will   deteriorate in quality or go in to default with resultant loss to the bank. This is the most obvious and most important risk to the banking industry in terms of potential losses. Credit risk is not confined to the risk that borrowers are unable to pay; it also includes the risk of payments being delayed, which can also cause problems for the bank. In order to protect their own interest and the wealth of bank depositors, banks need to investigate and monitor the activities of the will be and existing borrowers. Adequately managing of those risks related with credit is critical for the survival and growth of any financial institution. The present case study projects the effects of Non-Performing Assets on the Financial Performance of Commercial Banks in Ethiopia.  

scholarly journals Measuring and Managing Credit Risk for Chinese Microfinance Institutions

2018 ◽  
Vol 10 (7) ◽  
pp. 56
Author(s):  
Jie Li ◽  
Zhenyu Sheng
Keyword(s):  
Credit Risk ◽  
Predictive Power ◽  
Direct Method ◽  
Credit Scoring ◽  
Microfinance Institutions ◽  
Linear Discriminant ◽  
Management Level ◽  
Scoring Model ◽  
Subjective Appraisals ◽  
Credit Scoring Model

Chinese microfinance institutions need to measure and manage credit risk in a quantitative way in order to improve competitiveness. To establish a credit scoring model (CSM) with sound predictive power, they should examine various models carefully, identify variables, assign values to variables and reduce variable dimensions in an appropriate way. Microfinance institutions could employ both CSM and loan officer’s subjective appraisals to improve risk management level gradually. The paper sets up a CSM based on the data of a microfinance company running from October 2009 to June 2014 in Jiangsu province. As for establishing the model, the paper uses Linear Discriminant Analysis (LDA) method, selects 16 initial variables, employs direct method to assign variables and adopts all the variables into the model. Ten samples are constructed by randomly selecting records. Based on the samples, the coefficients are determined and the final none-standardized discriminant function is established. It is found that Bank credit, Education, Old client and Rate variables have the greatest impact on the discriminant effect. Compared with the same international models, this model’s classification effect is fine. The paper displays the key technical points to build a credit scoring model based on a practical application, which provides help and references for Chinese microfinance institutions to measure and manage credit risk quantitatively.

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